This letter presents a novel density-based control framework for shape formation in swarm robotic systems, operating exclusively on local perception without requiring communication or identity recognition among robots and targets. Inspired by Smoothed Particle Hydrodynamics (SPH), each robot estimates a local spatial density using kernel functions and regulates its motion based on the gradient of a constructed target density field. Unlike existing methods that depend on global coordination or explicit target assignments, the proposed approach enables fully distributed shape formation through local density regulation alone. By adjusting the reference density, the system supports autonomous scaling in both the number of robots and the geometric size of the target shape, seamlessly transitioning among shape-surrounding, double-layer contour, and boundary-conformal formations. A composite control law integrates density-driven forces, repulsive interactions for collision avoidance, and damping for dynamic stability. The effectiveness and robustness of the method are validated through extensive simulations and physical experiments with 50 robots under a motion capture laboratory setup. Results demonstrate the scalability, resilience, and practicality of the approach for decentralized swarm shape formation.

Collective evasion is critical in swarm robotics, enabling groups of simple robots to effectively elude external threats. Previous work on anti-predator behaviors relied on rigid evasion patterns coupled with velocity alignment, which lack adaptability when confronted with sustained and varied predatory attacks and further degrade anti-predation performance. Achieving adaptive collective evasion without predefined evasion rules and explicit velocity alignment is underexplored in swarm robotics. Here, we propose a density-based swarm model for adaptive collective evasion driven by field contextual sensing derived from positional information. By leveraging instantaneous density gradients and temporal fluctuations in density field, our approach enables the emergence of the fountain maneuver, wherein the group exhibits rapid spreading under predation followed by regrouping behind the threat. Compared to coordinated turning and rigid fountain maneuvers, our approach demonstrates better anti-predation performance through more dynamic and longer sustained fountain maneuvers under continuous attacks in both simulations and Human-In-The-Loop predation experiments with real robots, highlighting the practical advantages of adaptive collective evasion in swarm robotics.

The problem of self-organizing flexible wrapping for swarm robots refers to the spontaneous formation of stable, deformable, and adaptive structures by a robot swarm relying solely on local perception, without a global map or centralized control, to tightly enclose irregular or dynamic targets. This holds significant application value in fields such as environmental containment, disaster rescue, and wide-area monitoring. This paper proposes a self-organizing flexible wrapping method for swarm robots based on local density interactions. First, a local density field model integrating individual area weights is constructed to achieve the quantitative characterization of the target distribution space. Second, a weighted density is calculated based on the distributions of neighborhood robots and targets. Combined with diffusion and collision avoidance mechanisms, a distributed control law is designed to drive the swarm to adaptively converge towards the target boundaries while maintaining a uniform distribution. Finally, simulation and real-world experiments demonstrate that the proposed method can control the robot swarm to form a flexible wrapping structure. Compared with traditional methods, it significantly improves the conformity to complex target boundaries and overall adaptability, thereby verifying the feasibility and effectiveness of the proposed algorithm.